OpenRaiser/Intern-Atlas huggingface dataset:
DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction
Venue: arXiv cs.CL 2022
Year: 2022

This paper introduces DeepParliament, a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The proposed dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. Data collection, detailed statistics and analyses are provided in the paper. Moreover, we experimented with different types of models ranging from RNN to pretrained and reported the results. We are proposing two new benchmarks: Binary and Multi-Class Bill Status classification. Models developed for bill documents and relevant supportive tasks may assist Members of Parliament (MPs), presidents, and other legal practitioners. It will help review or prioritise bills, thus speeding up the billing process, improving the quality of decisions and reducing the time consumption in both houses. Considering that the foundation of the country's democracy is Parliament and state legislatures, we anticipate that our research will be an essential addition to the Legal NLP community. This work will be the first to present a Parliament bill prediction task. In order to improve the accessibility of legal AI resources and promote reproducibility, we have made our code and dataset publicly accessible at github.com/monk1337/DeepParliament
Considerations for meaningful sign language machine translation based on glosses
Venue: arXiv cs.CL 2022
Year: 2022

Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation. To address these issues, we put forward concrete recommendations for future research on gloss translation. Our suggestions advocate awareness of the inherent limitations of gloss-based approaches, realistic datasets, stronger baselines and convincing evaluation.
LSA-T: The first continuous Argentinian Sign Language dataset for Sign Language Translation
Venue: arXiv cs.CV 2022
Year: 2022

Sign language translation (SLT) is an active field of study that encompasses human-computer interaction, computer vision, natural language processing and machine learning. Progress on this field could lead to higher levels of integration of deaf people. This paper presents, to the best of our knowledge, the first continuous Argentinian Sign Language (LSA) dataset. It contains 14,880 sentence level videos of LSA extracted from the CN Sordos YouTube channel with labels and keypoints annotations for each signer. We also present a method for inferring the active signer, a detailed analysis of the characteristics of the dataset, a visualization tool to explore the dataset and a neural SLT model to serve as baseline for future experiments.
Cell Biomechanical Modeling Based on Membrane Theory with Considering Speed Effect of Microinjection
Venue: arXiv cs.RO 2022
Year: 2022

As an effective method to deliver external materials into biological cells, microinjection has been widely applied in the biomedical field. However, the cognition of cell mechanical property is still inadequate, which greatly limits the efficiency and success rate of injection. Thus, a new rate-dependent mechanical model based on membrane theory is proposed for the first time. In this model, an analytical equilibrium equation between the injection force and cell deformation is established by considering the speed effect of microinjection. Different from the traditional membrane-theory-based model, the elastic coefficient of the constitutive material in the proposed model is modified as a function of the injection velocity and acceleration, effectively simulating the influence of speeds on the mechanical responses and providing a more generalized and practical model. Using this model, other mechanical responses at different speeds can be also accurately predicted, including the distribution of membrane tension and stress and the deformed shape. To verify the validity of the model, numerical simulations and experiments are carried out. The results show that the proposed model can match the real mechanical responses well at different injection speeds.
FastCycle: A Message Sharing Framework for Modular Automated Driving Systems
Venue: arXiv cs.RO 2022
Year: 2022

Automated Driving Systems (ADS) have rapidly evolved in recent years and their architecture becomes sophisticated. Ensuring robustness, reliability and safety of performance is particularly important. The main challenge in building an ADS is the ability to meet certain stringent performance requirements in terms of both making safe operational decisions and finishing processing in real-time. Middlewares play a crucial role to handle these requirements in ADS. The way middlewares share data between the different system components has a direct impact on the overall performance, particularly the latency overhead. To this end, this paper presents FastCycle as a lightweight multi-threaded zero-copy messaging broker to meet the requirements of a high fidelity ADS in terms of modularity, real-time performance and security. We discuss the architecture and the main features of the proposed framework. Evaluation of the proposed framework based on standard metrics in comparison with popular middlewares used in robotics and automated driving shows the improved performance of our framework. The implementation of FastCycle and the associated comparisons with other frameworks are open sourced.
Semantic Table Detection with LayoutLMv3
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents an application of the LayoutLMv3 model for semantic table detection on financial documents from the IIIT-AR-13K dataset. The motivation behind this paper's experiment was that LayoutLMv3's official paper had no results for table detection using semantic information. We concluded that our approach did not improve the model's table detection capabilities, for which we can give several possible reasons. Either the model's weights were unsuitable for our purpose, or we needed to invest more time in optimising the model's hyperparameters. It is also possible that semantic information does not improve a model's table detection accuracy.
High-performance xPU Stencil Computations in Julia
Venue: arXiv cs.DC 2022
Year: 2022

We present an efficient approach for writing architecture-agnostic parallel high-performance stencil computations in Julia, which is instantiated in the package ParallelStencil.jl. Powerful metaprogramming, costless abstractions and multiple dispatch enable writing a single code that is suitable for both productive prototyping on a single CPU thread and production runs on multi-GPU or CPU workstations or supercomputers. We demonstrate performance close to the theoretical upper bound on GPUs for a 3-D heat diffusion solver, which is a massive improvement over reachable performance with CUDA.jl Array programming.
PyTorch Adapt
Venue: arXiv cs.LG 2022
Year: 2022

PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at https://www.github.com/KevinMusgrave/pytorch-adapt
Distributed Parallelization of xPU Stencil Computations in Julia
Venue: arXiv cs.DC 2022
Year: 2022

We present a straightforward approach for distributed parallelization of stencil-based xPU applications on a regular staggered grid, which is instantiated in the package ImplicitGlobalGrid.jl. The approach allows to leverage remote direct memory access and enables close to ideal weak scaling of real-world applications on thousands of GPUs. The communication costs can be easily hidden behind computation.
Controlled Language Generation for Language Learning Items
Venue: arXiv cs.CL 2022
Year: 2022

This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.
Sketch-and-solve approaches to k-means clustering by semidefinite programming
Venue: arXiv cs.LG 2022
Year: 2022

We introduce a sketch-and-solve approach to speed up the Peng-Wei semidefinite relaxation of k-means clustering. When the data is appropriately separated we identify the k-means optimal clustering. Otherwise, our approach provides a high-confidence lower bound on the optimal k-means value. This lower bound is data-driven; it does not make any assumption on the data nor how it is generated. We provide code and an extensive set of numerical experiments where we use this approach to certify approximate optimality of clustering solutions obtained by k-means++.
Mechanized Noninterference for Gradual Security
Venue: arXiv cs.PL 2022
Year: 2022

This paper presents the first machine-checked proof of noninterference for a language with gradual information-flow control, thereby establishing a rock solid foundation for secure programming languages that give programmers the choice between runtime versus compile-time enforcement. Along the way we uncovered a flaw in one of the noninterference proofs in the literature, and give a counterexample for one of the main lemmas. The particular language studied in this paper, $λ_{\mathtt{SEC}}^\star$, is based on the GLIO language of Azevedo de Amorim et al. [2020]. To make the design more accessible to other researchers, this paper contributes the first traditional semantics for the language, that is, we define compilation from $λ_{\mathtt{SEC}}^\star$ to a cast calculus and design a reduction semantics for the latter that includes blame tracking. In addition to the proof of noninterference, we also mechanize proofs of type safety, determinism, and that compilation preserves types.
Superpoint Transformer for 3D Scene Instance Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. It groups potential features from point clouds into superpoints, and directly predicts instances through query vectors without relying on the results of object detection or semantic segmentation. The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances. Through bipartite matching based on superpoint masks, SPFormer can implement the network training without the intermediate aggregation step, which accelerates the network. Extensive experiments on ScanNetv2 and S3DIS benchmarks verify that our method is concise yet efficient. Notably, SPFormer exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set in terms of mAP and keeps fast inference speed (247ms per frame) simultaneously. Code is available at https://github.com/sunjiahao1999/SPFormer.
MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data
Venue: arXiv cs.SD 2022
Year: 2022

Music structure analysis (MSA) systems aim to segment a song recording into non-overlapping sections with useful labels. Previous MSA systems typically predict abstract labels in a post-processing step and require the full context of the song. By contrast, we recently proposed a supervised framework, called "Music Structural Function Analysis" (MuSFA), that models and predicts meaningful labels like 'verse' and 'chorus' directly from audio, without requiring the full context of a song. However, the performance of this system depends on the amount and quality of training data. In this paper, we propose to repurpose a public dataset, HookTheory Lead Sheet Dataset (HLSD), to improve the performance. HLSD contains over 18K excerpts of music sections originally collected for studying automatic melody harmonization. We treat each excerpt as a partially labeled song and provide a label mapping, so that HLSD can be used together with other public datasets, such as SALAMI, RWC, and Isophonics. In cross-dataset evaluations, we find that including HLSD in training can improve state-of-the-art boundary detection and section labeling scores by ~3% and ~1% respectively.
On the Utility Recovery Incapability of Neural Net-based Differential Private Tabular Training Data Synthesizer under Privacy Deregulation
Venue: arXiv cs.LG 2022
Year: 2022

Devising procedures for auditing generative model privacy-utility tradeoff is an important yet unresolved problem in practice. Existing works concentrates on investigating the privacy constraint side effect in terms of utility degradation of the train on synthetic, test on real paradigm of synthetic data training. We push such understanding on privacy-utility tradeoff to next level by observing the privacy deregulation side effect on synthetic training data utility. Surprisingly, we discover the Utility Recovery Incapability of DP-CTGAN and PATE-CTGAN under privacy deregulation, raising concerns on their practical applications. The main message is Privacy Deregulation does NOT always imply Utility Recovery.
Practical Challenges in Indoor Mobile Recommendation
Venue: arXiv cs.HC 2022
Year: 2022

Recommendation systems are present in multiple contexts as e-commerce, websites, and media streaming services. As scenarios get more complex, techniques and tools have to consider a number of variables. When recommending services/products to mobile users while they are in indoor environments next to the object of the recommendation, variables as location, interests, route, and interaction logs also need to be taken into account. In this context, this work discusses the practical challenges inherent to the context of indoor mobile recommendation (e.g., mall, parking lot, museum, among others) grounded on a case and a systematic review. With the presented results, one expects to support practitioners in the task of defining the proper approach, technology, and notification method when recommending services/products to mobile users in indoor environments.
OK Computer Analysis: An Audio Corpus Study of Radiohead
Venue: arXiv cs.SD 2022
Year: 2022

The application of music information retrieval techniques in popular music studies has great promise. In the present work, a corpus of Radiohead songs across their career from 1992 to 2017 are subjected to automated audio analysis. We examine findings from a number of granularities and perspectives, including within song and between song examination of both timbral-rhythmic and harmonic features. Chronological changes include possible career spanning effects for a band's releases such as slowing tempi and reduced brightness, and the timbral markers of Radiohead's expanding approach to instrumental resources most identified with the Kid A and Amnesiac era. We conclude with a discussion highlighting some challenges for this approach, and the potential for a field of audio file based career analysis.
On the Envy-free Allocation of Chores
Venue: arXiv cs.GT 2022
Year: 2022

We study the problem of allocating a set of indivisible chores to three agents, among whom two have additive cost functions, in a fair manner. Two fairness notions under consideration are envy-freeness up to any chore (EFX) and a relaxed notion, namely envy-freeness up to transferring any chore (tEFX). In contrast to the case of goods, the case of chores remain relatively unexplored. In particular, our results constructively prove the existence of a tEFX allocation for three agents if two of them have additive cost functions and the ratio of their highest and lowest costs is bounded by two. In addition, if those two cost functions have identical ordering (IDO) on the costs of chores, then an EFX allocation exists even if the condition on the ratio bound is slightly relaxed. Throughout our entire framework, the third agent is unrestricted besides having a monotone cost function.
MegaBlocks: Efficient Sparse Training with Mixture-of-Experts
Venue: arXiv cs.LG 2022
Year: 2022

We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs. Our system is motivated by the limitations of current frameworks, which restrict the dynamic routing in MoE layers to satisfy the constraints of existing software and hardware. These formulations force a tradeoff between model quality and hardware efficiency, as users must choose between dropping tokens from the computation or wasting computation and memory on padding. To address these limitations, we reformulate MoE computation in terms of block-sparse operations and develop new block-sparse GPU kernels that efficiently handle the dynamism present in MoEs. Our approach never drops tokens and maps efficiently to modern hardware, enabling end-to-end training speedups of up to 40% over MoEs trained with the state-of-the-art Tutel library and 2.4x over DNNs trained with the highly-optimized Megatron-LM framework.
ClueWeb22: 10 Billion Web Documents with Visual and Semantic Information
Venue: arXiv cs.IR 2022
Year: 2022

ClueWeb22, the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high quality, large scale web corpus to support a range of academic and industry research, for example, in information systems, retrieval-augmented AI systems, and model pretraining. Compared with earlier ClueWeb corpora, the ClueWeb22 corpus is larger, more varied, of higher-quality, and aligned with the document distributions in commercial web search. Besides raw HTML, ClueWeb22 includes rich information about the web pages provided by industry-standard document understanding systems, including the visual representation of pages rendered by a web browser, parsed HTML structure information from a neural network parser, and pre-processed cleaned document text to lower the barrier to entry. Many of these signals have been widely used in industry but are available to the research community for the first time at this scale.
Effective Utilisation of Multiple Open-Source Datasets to Improve Generalisation Performance of Point Cloud Segmentation Models
Venue: arXiv cs.CV 2022
Year: 2022

Semantic segmentation of aerial point cloud data can be utilised to differentiate which points belong to classes such as ground, buildings, or vegetation. Point clouds generated from aerial sensors mounted to drones or planes can utilise LIDAR sensors or cameras along with photogrammetry. Each method of data collection contains unique characteristics which can be learnt independently with state-of-the-art point cloud segmentation models. Utilising a single point cloud segmentation model can be desirable in situations where point cloud sensors, quality, and structures can change. In these situations it is desirable that the segmentation model can handle these variations with predictable and consistent results. Although deep learning can segment point clouds accurately it often suffers in generalisation, adapting poorly to data which is different than the training data. To address this issue, we propose to utilise multiple available open source fully annotated datasets to train and test models that are better able to generalise. In this paper we discuss the combination of these datasets into a simple training set and challenging test set. Combining datasets allows us to evaluate generalisation performance on known variations in the point cloud data. We show that a naive combination of datasets produces a model with improved generalisation performance as expected. We go on to show that an improved sampling strategy which decreases sampling variations increases the generalisation performance substantially on top of this. Experiments to find which sample variations give this performance boost found that consistent densities are the most important.
On Robust Learning from Noisy Labels: A Permutation Layer Approach
Venue: arXiv cs.LG 2022
Year: 2022

The existence of label noise imposes significant challenges (e.g., poor generalization) on the training process of deep neural networks (DNN). As a remedy, this paper introduces a permutation layer learning approach termed PermLL to dynamically calibrate the training process of the DNN subject to instance-dependent and instance-independent label noise. The proposed method augments the architecture of a conventional DNN by an instance-dependent permutation layer. This layer is essentially a convex combination of permutation matrices that is dynamically calibrated for each sample. The primary objective of the permutation layer is to correct the loss of noisy samples mitigating the effect of label noise. We provide two variants of PermLL in this paper: one applies the permutation layer to the model's prediction, while the other applies it directly to the given noisy label. In addition, we provide a theoretical comparison between the two variants and show that previous methods can be seen as one of the variants. Finally, we validate PermLL experimentally and show that it achieves state-of-the-art performance on both real and synthetic datasets.